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Heterosis in the lactation curves of Girolando cows with emphasis on variations of the individual curves

Darlene dos Santos DaltroDepartamento de Zootecnia, Universidade Federal do Rio Grande do Sul – UFRGS, Porto Alegre, BrazilCorrespondence[email protected]

https://orcid.org/0000-0003-2278-8129 View further author information

Alessandro Haiduck PadilhaDepartamento de Zootecnia, Universidade Federal do Rio Grande do Sul – UFRGS, Porto Alegre, BrazilView further author information

Marcos Vinícius Gualberto Barbosa da SilvaEmbrapa Gado de Leite, Juiz de Fora, Brazil

https://orcid.org/0000-0001-5449-1413 View further author information

Elisandra Lurdes KernCentro de Ciências Agrárias, Universidade Estadual do Oeste do Paraná - UNIOESTE, Cascavel, BrazilView further author information

Diego Charles de Almeida SantosAssociação dos Criadores de Gado Holandês de Minas Gerais, Juiz de Fora, BrazilView further author information

João Claudio do Carmo PanettoEmbrapa Gado de Leite, Juiz de Fora, BrazilView further author information

Luís Telo da GamaFaculdade de Medicina Veterinária, Universidade Técnica de Lisboa, Lisboa, Portugal, Portugal

https://orcid.org/0000-0003-3894-3488 View further author information

Jaime Araujo CobuciDepartamento de Zootecnia, Universidade Federal do Rio Grande do Sul – UFRGS, Porto Alegre, Brazil

https://orcid.org/0000-0002-1414-4967 View further author information

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Figures & data

Table 1. Number of cows, herds and test day milk yield records as well as 305-day milk yield records (TMY) of genetic groups with different proportion of Holstein genes obtained from the Brazilian databases.

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Figure 1. Trajectory of lactation curves estimated by 305-day milk yield from database (TMY), Wood’s linear model (WDlin), Ali and Schaeffer’s model (ASH), Wood’s nonlinear model (WDnlin) and Wilmink model (WL) for 1/2H (A), 1/4H (B), 3/4H (C), 3/8H (D), 5/8H (E), 7/8H (F), G (G) and H (H) genetic groups.

Table 2. Estimated parameters (mean ± SE) of the lactation curve obtained from different nonlinear models, adjusted coefficient of determination (R ² _Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

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Table 3. Estimated parameters (mean ± SE) of the lactation curve obtained from different linear models, adjusted coefficient of determination (R² _Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

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Table 4. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P_2:1; P_3:1, P_3:2 and P_weller) and 305-day milk yield (TMY) for different genetic groups estimated by nonlinear models.

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Table 5. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P_2:1; P_3:1, P_3:2 and P_weller) and 305-day milk yield (TMY) for different genetic groups estimated by linear models.

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Table 6. Heterosis effect (mean ± SE) for Wilmink parameters (‘a’, ‘b’ and ‘c’), peak yield (PY), peak time (PT), persistency measures (P_2:1, P_3:1, and P_3:2) and 305-day milk yield (TMY) estimated Wilmink nonlinear model.

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Heterosis in the lactation curves of Girolando cows with emphasis on variations of the individual curves

Table 1. Number of cows, herds and test day milk yield records as well as 305-day milk yield records (TMY) of genetic groups with different proportion of Holstein genes obtained from the Brazilian databases.

Table 2. Estimated parameters (mean ± SE) of the lactation curve obtained from different nonlinear models, adjusted coefficient of determination (R ² _Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

Table 3. Estimated parameters (mean ± SE) of the lactation curve obtained from different linear models, adjusted coefficient of determination (R² _Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

Table 4. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P_2:1; P_3:1, P_3:2 and P_weller) and 305-day milk yield (TMY) for different genetic groups estimated by nonlinear models.

Table 5. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P_2:1; P_3:1, P_3:2 and P_weller) and 305-day milk yield (TMY) for different genetic groups estimated by linear models.

Table 6. Heterosis effect (mean ± SE) for Wilmink parameters (‘a’, ‘b’ and ‘c’), peak yield (PY), peak time (PT), persistency measures (P_2:1, P_3:1, and P_3:2) and 305-day milk yield (TMY) estimated Wilmink nonlinear model.

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Heterosis in the lactation curves of Girolando cows with emphasis on variations of the individual curves

Figures & data

Table 1. Number of cows, herds and test day milk yield records as well as 305-day milk yield records (TMY) of genetic groups with different proportion of Holstein genes obtained from the Brazilian databases.

Table 2. Estimated parameters (mean ± SE) of the lactation curve obtained from different nonlinear models, adjusted coefficient of determination (R 2 Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

Table 3. Estimated parameters (mean ± SE) of the lactation curve obtained from different linear models, adjusted coefficient of determination (R2 Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

Table 4. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P2:1; P3:1, P3:2 and Pweller) and 305-day milk yield (TMY) for different genetic groups estimated by nonlinear models.

Table 5. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P2:1; P3:1, P3:2 and Pweller) and 305-day milk yield (TMY) for different genetic groups estimated by linear models.

Table 6. Heterosis effect (mean ± SE) for Wilmink parameters (‘a’, ‘b’ and ‘c’), peak yield (PY), peak time (PT), persistency measures (P2:1, P3:1, and P3:2) and 305-day milk yield (TMY) estimated Wilmink nonlinear model.

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Table 2. Estimated parameters (mean ± SE) of the lactation curve obtained from different nonlinear models, adjusted coefficient of determination (R ² _Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

Table 3. Estimated parameters (mean ± SE) of the lactation curve obtained from different linear models, adjusted coefficient of determination (R² _Adj), root mean square error (RMSE) and Akaike information criterion (AIC).

Table 4. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P_2:1; P_3:1, P_3:2 and P_weller) and 305-day milk yield (TMY) for different genetic groups estimated by nonlinear models.

Table 5. Estimated (mean ± SE) of peak yield (PY), peak time (PT), persistency (P_2:1; P_3:1, P_3:2 and P_weller) and 305-day milk yield (TMY) for different genetic groups estimated by linear models.

Table 6. Heterosis effect (mean ± SE) for Wilmink parameters (‘a’, ‘b’ and ‘c’), peak yield (PY), peak time (PT), persistency measures (P_2:1, P_3:1, and P_3:2) and 305-day milk yield (TMY) estimated Wilmink nonlinear model.